Zero Trust for AI Agents: Practical Patterns for Least Privilege Copilots
AI agents need the same least-privilege treatment as any privileged identity. Here's how to apply zero trust principles to copilots and autonomous agents.
Deliverables
What you get.
Integration threat modeling
We map every data path, identify leak risks, and design controls before a single prompt hits production.
RAG done right
Scoped retrieval with per-role indices, PII redaction at embed time, and provenance logging — so every answer can be traced to its source.
Agent identity & least privilege
Every agent gets its own scoped token, its own audit trail, and a human-in-the-loop boundary for any action that touches customer data.
Prompt-injection defenses
System-prompt hardening, output validation, and safe rendering — with regression tests that run on every deploy.
LLM gateway & logging
Central broker so every prompt/response is logged, rate-limited, and policy-checked — without each app team reinventing the wheel.
FAQ
Common questions.
Do you work with OpenAI, Anthropic, and local models?
All of the above. The controls are the same. What changes is where inference happens and how data egress is governed.
How do you handle PHI, PCI, or FERPA data?
We treat regulated data as out-of-bounds for model context by default. Retrieval is tokenized and redacted, and integrations route through policy-aware proxies.
Can you retrofit this to an existing integration?
Yes. Most engagements start with an audit of what already exists, then a prioritized hardening roadmap.
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